{"uid":"cap_egxDZPPN3oKezPyaQcSeq","slug":"trading-journal-observations-numeric-distribution-explicit-missing-678d0d04","name":"Trading Journal Observations — Numeric Distribution + Explicit Missing-Value Fill","description":"Trading journal observations — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill for supplied trading journal observations records (entry_price, exit_price). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-finance-09-085?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","instrument_group","date","entry_price","exit_price"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"exit_price":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"entry_price":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"instrument_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","exit_price":1,"entry_price":10,"instrument_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","exit_price":2,"entry_price":20,"instrument_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","exit_price":3,"entry_price":15,"instrument_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","exit_price":4,"entry_price":30,"instrument_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["entry_price","exit_price"],"type":"string","default":"entry_price"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_wT1L3L_iFHa61lgQHsIqh","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Analyzes up to 200 trading journal rows by computing numeric distribution statistics and filling missing values for entry_price and exit_price fields using zero, mean, or median imputation.","exampleAgentPrompt":"Analyze the distribution of entry_price values and fill any missing entry_price fields using median imputation for these 5 trading journal rows: row-1 on 2025-01-10 in equities with entry 102.5 and exit 105.0, row-2 on 2025-01-11 in equities with entry null and exit 98.0, row-3 on 2025-01-12 in forex with entry 1.25 and exit 1.27, row-4 on 2025-01-13 in forex with entry 1.22 and exit null, row-5 on 2025-01-14 in equities with entry 110.0 and exit 112.5.","exampleUseCases":[{"title":"Impute missing exit prices in trade log","prompt":"I have 50 trade journal records where some exit_price values are null — can you fill those missing exit prices using the mean of the available ones and also show me the overall price distribution?"},{"title":"Distribution audit before strategy review","prompt":"Before my weekly strategy review, run a numeric distribution report on entry_price for these 120 trading journal rows across my equities and forex groups so I can see spreads, min, max, and central tendency."},{"title":"Zero-fill missing entries for backtesting pipeline","prompt":"For these 80 trading journal observations, fill any missing entry_price values with zero and give me the numeric distribution report — I need clean data before feeding it into my backtesting pipeline."}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, median, percentiles, spread) for the specified price field across up to 200 rows, and (2) an explicit missing-value fill report showing which rows had null values and what imputed values were applied using the chosen method (zero, mean, or median). All computation is local with no external data lookups.","failureModes":["Exceeding 200 rows causes rejection — keep input to maxItems limit","Invalid date format (not YYYY-MM-DD) triggers schema validation error","Providing a field value outside the enum (entry_price, exit_price) returns a validation error","Providing a method outside the enum (zero, mean, median) returns a validation error","Empty rows array (minItems:1 not met) causes request failure","row_id missing or blank triggers required-field validation error","Numeric values outside the allowed range (±1 trillion) are rejected"],"whenToPreferThis":"Choose this endpoint when you have trading journal data with entry_price and exit_price columns and need both a statistical distribution summary and a missing-value imputation report in a single call for up to 200 rows. It is ideal for pre-processing trade logs before backtesting or strategy analysis when you want offline, deterministic computation with no external data dependencies. Prefer it over general-purpose analytics APIs when you specifically need the combination of distribution profiling and explicit null-fill for financial price fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T00:35:28.002Z","isFirstParty":false,"canonicalSlug":"trading-journal-observations-numeric-distribution-explicit-missing-678d0d04"}